Continuous all k nearest neighbor queries in smartphone networks
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Georgios Chatzimilioudis Demetrios Zeinalipour-Yazti Marios D. Dikaiakos. Wang-Chien Lee. “ Continuous All k Nearest Neighbor Queries in Smartphone Networks ”. Tuesday, July 24, 2012 13 th IEEE Int. Conference on Mobile Data Management (MDM ’ 12), Bangalore, India. Smartphones.

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Continuous all k nearest neighbor queries in smartphone networks

Georgios Chatzimilioudis

Demetrios Zeinalipour-Yazti

Marios D. Dikaiakos

Wang-Chien Lee

“Continuous All k Nearest Neighbor Queries in Smartphone Networks”

Tuesday, July 24, 2012

13th IEEE Int. Conference on Mobile Data Management (MDM’12), Bangalore, India


Smartphones

Smartphones

  • Smartphone: A powerful sensing device!

    • Processing: 1.4 GHzquad core (Samsung Exynos)

    • RAM & Flash Storage:2GB & 48GB, respectively

    • Networking: WiFi, 3G (Mbps) / 4G (100Mbps–1Gbps)

    • Sensing: Proximity, Ambient Light, Accelerometer, Microphone, Geographic Coordinates based on AGPS (fine), WiFi or Cellular Towers (coarse).

  • In-House Applications!

Airplace

(MobiSys’12, MDM’12)

SmartTrace

(ICDE’09,MDM’09,TKDE’12)

SmartP2P

(MDM’11, MDM’12)


Motivation

Motivation

  • Crowdsourcing with Smartphones

    • A smartphone crowd is constantly moving and sensing providing large amounts of opportunistic data that enables new services and applications

"Crowdsourcing with Smartphones", Georgios Chatzimiloudis, Andreas Konstantinides, Christos Laoudias, Demetrios Zeinalipour-Yazti, IEEE Internet Computing, Special Issue: Crowdsourcing (Sep/Oct 2012), accepted May 2012. IEEE Press, 2012


Continuous k nearest neighbor queries

Continuous k Nearest Neighbor Queries

Find 2 Closest Neighbors for 1 User


Motivation1

Motivation

“Create a Framework for Efficient Proximity Interactions”

Screenshots from a prototype system we've implemented for Windows Phone

http://www.zegathem.com/


Continuous all k nearest neighbor queries

Continuous All k Nearest Neighbor Queries

Find 2 Closest Neighbors for ALL User


Presentation outline

Presentation Outline

  • Motivation

  • System Model and Problem Formulation

  • Proximity Algorithm

  • Experimental Evaluation

  • Future Work


System model

System Model

  • A set of users moving in the plane of a region (u1, u2, … un)

  • Area covered by a set of Network Connectivity Points (NCP)

    • Each NCP creates the notion of a cell

    • W.l.o.g., let the cell be represented by a circular area with an arbitrary radius

  • A mobile user u is serviced at any given time point by one NCP.

  • There is some centralized service, denoted as QP (Query Processor), which is aware of the coverage of each NCP.

  • Each user u reports its positional information to QP regularly

Query Processor

QP

u3.

u2.

.u4

u0.

C

.u1

u6.

.

u7

u5.


Problem definition

Problem Definition

  • Definition of the CAkNN problem:

    Given a set U of nusers and their location reports ri,t ∈ R at timestep t ∈ T , then the CAkNN problem is to find in each timestep t ∈ T and for each user ui ∈ U the k objects Usol ⊆ U − uisuch that for all other objects uo ∈U − Usol − ui, dist(uk, ui) ≤ dist(uo, ui) holds

u3.

Query

Processor

u2.

.u4

u0.

C

.u1

u6.

.

u7

u5.


Na ve solution related work

Naïve Solution / Related Work

Find 2-NN for u0at timestep t.

For u5?

Query Processor

Look inside your cell!

u3.

WRONG!

(u1 closer)

u2.

.u4

u0.

C

.u1

TOO EXPENSIVE!

u6.

.

u7

u5.

Perform iterative deepening!


Related work

Related Work

  • Existing algorithms for CkNN (not CAkNN)

    Yu et al. (YPK) 11 and Mouratidis et al. (CPM) 12

  • Stateless Version: Iteratively expand the search space for each user into neighboring cells to find the kNN (like previous slide)

  • Stateful Version: Improve the stateless version by utilizing previous state, under the following assumptions:

Not Appropriate for CAkNN

  • i) Static Querying User (i.e., designated for point queries)

  • ii) Target users move slowly (i.e., state does not decay)

  • iii) Few Target users

11. Yu, Pu, Koudas. “Monitoring k-nearest neighbor queries over moving objects,” ICDE ’05

12. Mouratidis, Papadias, Hadjieleftheriou, “Conceptual partitioning: an efficient method for continuous nearest neighbor monitoring,” SIGMOD ’05.


Presentation outline1

Presentation Outline

  • Motivation

  • System Model and Problem Formulation

  • Proximity Algorithm

  • Experimental Evaluation

  • Future Work


Proximity overview

Proximity Overview

  • The first specialized algorithm for Continuous All k-NN (CAkNN) queries

  • Important characteristics:

    • Stateless (i.e., optimized for high mobility)

    • Batch processing (i.e., with search space searching)

    • Parameter-free (i.e., no tuning parameters)

  • Generic operator for proximity-based queries

    • See Crowdcast application presented later.


Proximity outline

Proximity Outline

  • For every timestep:

  • Initialize a k+-heap for every cell

  • Insert every user’s location report to every k+-heap

    • Notice that k+-heap is a heap-based structure and most location reports will be dropped as a result of an insert operation

  • For every user scan the k+-heap of his cell to find his k-NN

  • u3.

    Query

    Processor

    u2.

    .u4

    u0.

    C

    .u1

    u6.

    .

    u7

    u5.


    Intuition behind proximity

    Intuition behind Proximity

    • Users in a cell will share the samesearch space(search space sharing)

    • Compute 1 search space per cell only!

    Query

    Processor

    u3.

    Cell

    u2.

    .u4

    u0.

    Search Space

    (contains the right answers for all users in C)

    C

    .u1

    u6.

    .

    u7

    u5.


    Proximity k heap

    Proximity k+-heap

    • The k+-heap structure for a cell

    k+-heap structure

    U

    list

    K

    top-k heap

    B

    ordered list

    All users inside the cell

    K nearest users outside the cell

    Beyondk nearest outside users (correctness)


    K heap construction for cell c

    k+-heap Construction (for Cell C)

    k+-heap structure

    Assume k=2.

    U

    list

    K

    top-k heap

    B

    ordered list

    u3.

    u2.

    .u4

    u0.

    C

    .u1

    u6.

    .

    u7

    u5.


    K heap construction for cell c1

    k+-heap Construction (for Cell C)

    k+-heap structure

    Assume k=2.

    U

    list

    K

    top-k heap

    B

    ordered list

    u3.

    u2.

    .u4

    u0.

    Diameter + dk

    C

    .u1

    u6.

    .

    u7

    u5.

    x.

    2 nearest users to cell


    Presentation outline2

    Presentation Outline

    • Motivation

    • System Model and Problem Formulation

    • Proximity Algorithm

    • Experimental Evaluation

    • Future Work


    Experimental methodology

    Experimental Methodology

    • Dataset:

      • Oldenburg Dataset:

        • Spatiotemporal generator with roadmap of Oldenburg as input (25km x 25km)

        • 5000 maximum users (Oldenburg population: 160.000)

      • Manhattan Dataset:

        • Vehicular mobility generator with roadmap of Manhattan as input (3km x 3km)

        • 500 users

    • Network connectivity points (NCPs) uniformly in space

      • Communication ranges 1km, 4km and 16km for the Oldenburg dataset and ranges 1km and 4km for the Manhattan dataset

    • Query: CAkNN

    • Comparison: Proximity vs YPK vs CPM (using the optimal parameter value for YPK and CPM)


    Proximity experiments

    Proximity - Experiments

    • Bottleneck for Proximity: build time

    • Bottleneck for the adapted YPK and CPM: search time


    Proximity experiments1

    Proximity - Experiments

    • Benefit of Proximity in the search time

    • Proximity is scalable with respect to k

      • Search space for Proximity is not proportional to k like YPK, CPM


    Proximity experiments2

    Proximity - Experiments

    • Proximity: build time drops as k increases!

    • Machine’s memory scheduler makes more efficient use of buffers when the search spaces are larger


    Proximity experiments3

    Proximity - Experiments

    • Proximity scales with the number of users

    • Proximity outperforms YPK, CPM by an order of magnitude

    • Proximity does not converge to YPK, CPM for higher values of Nmax.


    The crowdcast application suite

    The Crowdcast Application Suite

    Crowdcast Suite

    PROXIMITY

    WIN7 API

    Ranked 3rd in Microsoft’s ImagineCup contest local competition (to appear in the next demo session!)


    Crowdcast msgcast

    Crowdcast: MsgCast

    MsgCast

    • Location-based Chat Channel (Post / Follow)

    • - Provide Guidelines

    "Get your location-based questions answered!"


    Crowdcast helpcast

    Crowdcast: HelpCast

    HelpCast

    "The Ubiquitous Help Platform for Anyone in Need”


    Crowdcast eyecast

    Crowdcast: EyeCast

    EyeCast

    • Disaster Recovery Operations

    • Indoor Video Conferencing Network

    "Extend your Vision beyond your 2 eyes!"


    Presentation outline3

    Presentation Outline

    • Motivation

    • System Model and Problem Formulation

    • Proximity Algorithm

    • Experimental Evaluation

    • Future Work


    Proximity future work

    Proximity – Future Work

    • Privacy extensions (e.g., spatial cloaking, strong privacy)

    • Parallelizing server computation

    • Proximity for cloud server

    • User-defined k values


    Future work smartlab

    Future Work: SmartLab

    SmartLab.cs.ucy.ac.cy

    Install APK,

    Upload File,

    Reboot, Screenshots, Monkey Runners, etc.…

    Programming cloud for the development of smartphone network applications & protocols as well as experimentation with real smartphone devices.

    "Demo: A Programming Cloud of Smartphones", A. Konstantinidis, C. Costa, G. Larkou and D. Zeinalipour-Yazti, "Demo at the 10th International Conference on Mobile Systems Applications and Services" (Mobisys '12), Low Wood Bay Lake District UK, 2012


    Future work smartlab1

    Future Work: SmartLab


    Continuous all k nearest neighbor queries in smartphone networks1

    Thanks!

    Questions?

    “Continuous All k Nearest Neighbor Queries in Smartphone Networks”

    Georgios Chatzimilioudis

    Demetrios Zeinalipour-Yazti

    Marios D. Dikaiakos

    Wang-Chien Lee

    Tuesday, July 24, 2012

    13th IEEE Int. Conference on Mobile Data Management (MDM’12), Bangalore, India


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